Learning and Exploring Motor Skills with Spacetime Bounds

dc.contributor.authorMa, Li-Keen_US
dc.contributor.authorYang, Zeshien_US
dc.contributor.authorTong, Xinen_US
dc.contributor.authorGuo, Bainingen_US
dc.contributor.authorYin, KangKangen_US
dc.contributor.editorMitra, Niloy and Viola, Ivanen_US
dc.date.accessioned2021-04-09T08:00:34Z
dc.date.available2021-04-09T08:00:34Z
dc.date.issued2021
dc.description.abstractEquipping characters with diverse motor skills is the current bottleneck of physics-based character animation. We propose a Deep Reinforcement Learning (DRL) framework that enables physics-based characters to learn and explore motor skills from reference motions. The key insight is to use loose space-time constraints, termed spacetime bounds, to limit the search space in an early termination fashion. As we only rely on the reference to specify loose spacetime bounds, our learning is more robust with respect to low quality references. Moreover, spacetime bounds are hard constraints that improve learning of challenging motion segments, which can be ignored by imitation-only learning. We compare our method with state-of-the-art tracking-based DRL methods. We also show how to guide style exploration within the proposed framework.en_US
dc.description.number2
dc.description.sectionheadersLearning Pose Manifolds and Motor Skills
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume40
dc.identifier.doi10.1111/cgf.142630
dc.identifier.issn1467-8659
dc.identifier.pages251-263
dc.identifier.urihttps://doi.org/10.1111/cgf.142630
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf142630
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectAnimation
dc.subjectPhysical simulation
dc.subjectTheory of computation
dc.subjectReinforcement learning
dc.titleLearning and Exploring Motor Skills with Spacetime Boundsen_US
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